Getting cited early: influence of visibility strategies, structure, and focal system on early citation rates
Bibliographic record
Abstract
Abstract Elucidating factors that contribute to citation rates of scientific articles can help scientists write manuscripts that have a stronger influence on their scientific field and are accessible to a broad audience. Using a cohort of 778 articles published in The Journal of Wildlife Management from 2011–2015, we examined how visibility strategies, article structure, and focal system (all factors authors can predominantly control) influenced the accumulation of citations over various time frames within the first 5 years after publication, and the number of days until an article received its first citation. Visibility strategies (e.g., open access, increasing the Altmetric Attention Score, and self‐citations) all influenced the number of citations accrued following publication. Citations were more stochastic 1 year following publication compared to 5 years following publication, with only 20.1% of papers receiving a citation after 1 year compared to 92.5% of papers receiving a citation after 5 years. Our model explained much more of the variation after 5 years compared to after only 1 year ( R 2 = 0.57 and 0.12, respectively). The number of factors significantly associated with citation rates increased as the timeframe of our analysis increased. After 5 years, factors associated with article structure (e.g., number of references), focal system (e.g., methods papers), and visibility all increased citation counts of papers. Our work suggests citation rates within wildlife ecology are influenced by a number of controllable factors, and that authors pursuing a variety of visibility strategies can increase the influence of an article on science and management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".